Various remote sensing applications generate imagery of the earth and determining the geographic location of each pixel in the imagery increases the usefulness of the data. The accurate location and pointing of each pixel is a two-part process. The first part, image navigation, determines the location of a pixel within an image relative to an earth-referenced latitude and longitude. The second part, image registration, entails maintaining the location of the pixels within an image and repeated images to their earth-referenced latitude and longitude. This unique process, image navigation and registration (INR), yields imaging data on a precisely located, fixed-earth coordinate grid.
One INR approach, “landmark registration,” matches landmasses in the image against landmass truth data. Landmasses are separated by bodies of water such as oceans, seas, lakes, and rivers; a “shoreline” or, alternatively “coastline,” is defined as the boundary between landmasses and water. Shorelines are often prominently visible features in remote sensing imagery and “shoreline registration” uses these land/water boundaries as image features to accomplish accurate landmark registration.
Landmark registration techniques are separated into two general classes—image-based registration and shoreline-based registration. Image-base landmark registration correlates the collected image with earlier collected reference images whose landmarks have been earth-referenced. Image-based correlation techniques are impacted by changes in lighting angle between images as well as scenic changes over different seasons. Additionally, image-based registration methods have difficulties with day-to-night land/water inversion that occurs in infrared imagery.
Shoreline-based registration uses any of the several available shoreline vector truth databases and compares the location of shoreline edges in the collected image with shoreline truth positions in the database. Shoreline-based techniques are insensitive to many of the shortcomings of the image-based landmark registration, such as dependence on lighting conditions. However, the existing vector-based INR techniques have several shortcomings including the use of cloud detection and mitigation algorithms, inaccuracies from resolution differences between collected imagery and the vector database, and plotted shoreline vector truth image do not accurately represent edge characteristics in collected imagery.
One inherent challenge to landmark navigation is that at any point in time the Earth is 70% covered in clouds that occlude part or all of a landmark being used for navigation. In order to mitigate this issue, cloud detection algorithms discount landmarks that are impacted by clouds. Most of the current state-of-the-art landmark navigation algorithms perform landmark registration by extracting landmark neighborhood sub-images, masking pixels containing clouds, retrieving pertinent coastline truth data from a landmark library, registering the sub-image to the coastline truth data using various methods, and computing a quality metric to assess the landmarks registration reliability.
One of the significant problems in landmark registration occurs when cloudy pixels have not been masked appropriately. The sole purpose of cloud detection in landmark navigation is to prevent errors caused by processing landmark regions that are obscured by clouds. The best published algorithms for cloud detection report an accuracy rate between 70% and 87% depending on day vs. night and land vs. ocean. An operational system using cloud detection based on these techniques could find itself using invalid data in computing landmark registration in at least ten percent of the cases, affecting scene navigation accuracy. Such systems require further algorithms to determine the validity of landmark navigation measurements, which in themselves may be in error.
One example of a cloud masking landmark registration method is the AutoLandmark utility that is used for Geostationary Operational Environmental Satellite (GOES) weather imagery. In the AutoLandmark algorithm, coastline edges are detected in the scene image with a Sobel operator using an absolute value norm. Cloud detection is used to mask off regions of clouds in the image. Then, a similarity measure is calculated for a set of offset positions by summing the edge image at the locations of the vector coastline. This effectively correlates the coastline vector with the edge image without including data from masked cloud regions. A triangular interpolation is used to calculate the effective edge location response at each coastline vector point.
Another weakness of landmark registration algorithms is that there is no mechanism to actively reduce the registration quality score when a portion of the image landmark does not match the shape of the landmark truth data. The lesser ability to differentiate between landmarks having similar shapes increases the probability of erroneously matching to the wrong landmark.
Lewis Fry Richardson was first to recognize that the length of a coastline is dependent on the unit measure being applied to the measurement. His work later led Mandelbrot to the notion of fractal dimension. This feature of coastlines is also important when imaging coastlines at various imaging resolution. At a high resolution, fine-edge features that are represented at lower resolution contribute to edge blur. This property is important in landmark registration where the truth data is available as a series of line segments at a given resolution and is used to plot an edge image at the required scale and perspective for correlation against remote-sensing imagery. Not taking this property into account contributes to registration error in landmark registration.
This same factor applies for algorithms that plot the shoreline vector truth data into an image for correlation with the edge image of the collected image. The standard plotting algorithms for generating line plots of vector data have been designed for providing a pleasing graphic of data, not to simulate the appearance of edges contained in an image. Plot images generated by these routine will giving the improper relative importance of sections of the shoreline over other sections leading to correlation location inaccuracies.